The genomes of some pathogenic viruses are made up of RNA that can be involved directly in the production of proteins crucial to viral reproduction and infection. These fast-mutating RNA viruses showed a high incidence of cross-infections among different species by variant forms (Plant et al. 2005). It is feasible to perform computational analyses on the viral genome and obtain useful information which give clues to the origin, natural reservoir, and evolution of the virus, contributing to the understanding of the immune response to these viruses and the pathogenesis of viral diseases and facilitate the development of antiviral drugs. We have recently reported that regions in coronavirus RNA genomes with statistically significant clusters of palindromes are associated with the presence of stem-loops or pseudoknots (Chew et al 2004). These RNA structures have been suggested to be responsible for frame-shifting mechanisms during gene expression, where two different proteins can be produced in the same region just by shifting the reading frame by one base. We hypothesize that by exploiting the correlation between nonrandom clusters of close inversions with stem-loop and pseudoknot structures in RNA molecules, an efficient and utilitarian tool for predicting secondary structures on RNA viral genomes can be developed using current heterogeneous Grid Computing technology. We propose a four-year project to evaluate this hypothesis focusing on coronaviruses and influenza viruses with specific aims to: (1) Establish a statistics-based algorithm to locate RNA segment containing nonrandom clusters of close inversions. (2) Develop a strategy for cutting of the viral genome sequences into segments of length no greater than 200 bases. (3) Construct a software prototype for RNA secondary structure prediction using Grid Computing technology. (4) Implement user facilities for the software and predict genome structures in coronaviruses and Influenza viruses. For this project, our objective is to produce the prototype of an RNA secondary structure prediction system on a grid of heterogeneous, distributed computers. The software will be publicly accessible through a web portal. Our long-term goal is to develop a set of open-source computational tools in a Grid Computing environment to accurately predict genome structures and dynamics in RNA viruses and their interactions with cellular RNA. This will provide information to be used by virologists to design finely tuned experiments to study RNA viruses and their pathogenic interactions with their hosts, especially when time is limiting in combating new infectious viral diseases.